import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt


# 增加层数 inputs是输入数据，也就是每一个样本的规模，
def add_layer(inputs, in_size, out_size, activation_function=None):
    # insize行 outsize列 1 10
    Weights = tf.Variable(tf.random_normal([in_size, out_size]))

    # 1行outsize列，推荐不是0，在每一步骤训练中，都会有变化
    biases = tf.Variable(tf.zeros([1, out_size])) + 0.1

    # y=wx+b  1*in_size  insize*out_size   -->  1*outsize
    Wx_plus_b = tf.matmul(inputs, Weights) + biases
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)
    return outputs


# ~~reshape([10,1]) 一个特性有300个例子
x_data = np.linspace(-1, 1, 300)[:, np.newaxis]
# 噪点，避免规整数据
noise = np.random.normal(0, 0.05, x_data.shape)
# y=x^2+b+噪点
y_data = np.square(x_data) - 0.5 + noise

# None 无论进来多少组例子都可以,dtype要定义
xs = tf.placeholder(tf.float32, [None, 1])
ys = tf.placeholder(tf.float32, [None, 1])

# 输入层1个神经元，因为输入数据就一个数字
# 隐藏层10个神经元
# 输出层一个神经元，因为输出数据就一个数字
L1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
prediction = add_layer(L1, 10, 1, activation_function=None)

# 计算loss
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1]))

# 优化参数
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init = tf.global_variables_initializer()  # 替换成这样就好
sess = tf.Session()
sess.run(init)


# 可视化
fig=plt.figure()
ax=fig.add_subplot(1,1,1)
# 输入数据
ax.scatter(x_data,y_data)
# 可保留画布
plt.ion()
plt.show()

for i in range(1000):
    sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
    if i % 50 == 0:
        # print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))
        # try-catch模块
        try:
            ax.lines.remove(lines[0])
        except Exception:
            pass
        prediction_value=sess.run(prediction,feed_dict={xs:x_data})
        lines=ax.plot(x_data,prediction_value,'r-',lw=5)
        plt.pause(0.1)